Cache-efficient multigrid algorithms

Multigrid is widely used as an efficient solver for sparse linear systems arising from the discretization of elliptic boundary value problems. Linear relaxation methods such as Gauss-Seidel and Red-Black Gauss-Seidel form the principal computational component of multigrid, and thus affect its efficiency. In the context of multigrid, these iterative solvers are executed for a small number of iterations (2-8). We exploit this property of the algorithm to develop a cache-efficient multigrid method, by focusing on improving the memory behavior of the linear relaxation methods. The efficiency in our cache-efficient linear relaxation algorithm comes from two sources: reducing the number of data cache and TLB misses, and reducing the number of memory references by keeping values register-resident. Our optimizations are applicable to multigrid applied to linear systems arising from constant coefficient elliptic PDEs on structured grids. Experiments on five modern computing platforms show a performance improvement of 1.15-2.7 times over a standard implementation of Full Multigrid V-Cycle.